Collaborative Innovation in EAM Master Data Quality

February 26, 2014 Jody McDonnell

Image_Enterprise Asset Management for Oil and Gas

The SAP-Centric EAM conference in Austin, TX is celebrating its 10th year of bringing together the best Enterprise Asset Management (EAM) minds to collaborate and network with counterparts across many industries, including managers and directors of their respective organizations over topics such as Master Data Quality.  Over the course of three days, hundreds of professionals listen to 60+ speakers give eight keynotes and 36 session presentations and case studies.  We’re currently at the end of Day One, and I can already see that these next few days in Austin are all about people—beginners and experts alike—helping each other.

This spirit of collaboration was evident from the very first session.  The conference began with a series of roundtable discussions that functioned as icebreakers, opening up conversations and identifying common problems.  There were five to choose from:  Planning & Scheduling, Reporting & Analytics, Mobility, Master Data, and Fleet.  I chose to attend the Master Data roundtable, since this is the area where I have the most experience and passion.  Jeff Smith, EAM Business Process Analyst at Fairfax Water, and Kim Ronan, Team Lead of Operations Asset Management at Alliance Pipeline, facilitated the discussion by posing questions and talking points to the attendees, who were then encouraged to address questions and thoughts to the whole group.  We began by defining types of EAM master data in various industries:

  • Functional Locations
  • Equipment
  • Bill of Materials
  • Work Centers
  • Classes/Characteristics
  • Material Master

These functional elements are the centerpieces of the EAM world.  As we named and defined these types, it became immediately evident that these necessary components of the machine can be huge pain points within every organization, regardless of the specific industry or size of the company.  If there was anyone in this session that felt alone in the battle toward better master data, they were quickly put at ease.  All organizations struggle to define and continually refine business processes around these data elements.  Without well-defined business processes, data quality deteriorates.

Common Master Data Issues

Most of the roundtable involved question-and-answer interactions, in which the attendees presented real business process issues and collaborated with other EAM professionals in the room to identify possible solutions.  Here are just a few of the discussed issues, all of which show a concern for data quality:

  • Handoff from capital projects to the EAM group
  • Inheriting assets from merger and acquisition
  • Proper disposal of equipment
  • Maintaining classes/characteristics standards

Master data is the core foundation of any organization that utilizes EAM.  All in the room agreed that having accurate master data is fundamental to running a successful business and minimizing process interruption.  The baseline requirement for uptime in a factory, utility, etc., is having equipment that is functional and properly maintained.  Only then can the manufacturing and production processes succeed.

Managing Data Quality

The last topic of conversation concerned data integrity.  The question was “Who reviews and monitors data quality?”  The first answer surprised me: business analysts.  The main method, at least for those organizations represented in the room, for preserving data integrity didn’t involve any automated process.  After further discussion, it became apparent that though there are many different variations of manual and automated methods being used across different organizations, most users trust themselves and their own input far more than any automation they’ve encountered.

While most everyone agrees that an automated approach to data and information governance remains ideal, I believe that automated process must go through a rigorous proving phase in order to earn the trust of the user.  Simply owning or partially implementing a product is not sufficient to achieve the desired level of quality.  There are many solutions out there, but they all center around the same elements.  Is this product compatible with our ERP system?  Is it capable of adhering to our unique processes and ways of working?   Who can help us implement it properly?  After these questions are asked and answered comes the “show me” phase.

Trust is results driven.  In the end, a reliable, repeatable, trusted data and information governance solution is what everyone wants and needs to ensure that you keep an ongoing quality of data, and of course to make sure it is Business-Ready.

 

About the Author

Jody McDonnell

Pre-Sales Consultant at BackOffice Associates

Follow on Twitter More Content by Jody McDonnell
Previous Article
The Three Root Causes of Poor Big Data Quality
The Three Root Causes of Poor Big Data Quality

Poor data quality can be traced back to three root causes. These include the initial data load, application...

Next Article
The Need for Big Data Governance Revisited
The Need for Big Data Governance Revisited

In a recent interview with Timo Elliot on the need for big data governance, Richard Neale touches on the mo...

×

Get Tips, How To's and Great Reads Delivered Monthly To Your Inbox

Thank you for subscribing!
Error - something went wrong!